Generally, data entry into computers is performed using keyboards. The complexity and size of keyboards depend on the number and type of characters present in a script. Typically, phonetic scripts, such as Indic, Sinhalese, Burmese, Thai, Vietnamese, and the like tend to have large and/or complex character sets. Such scripts can present great difficulties in the design as well as use of these keyboards. For example, Indic scripts have nearly 30 to 40 consonants, 12 to 15 vowels, and about 12 to 15 phonetic modifiers and half consonant modifiers. It can be envisioned that various combinations of these consonants, vowels, and modifiers can result in forming a significantly large character set. To accommodate such a large character set, the amount of keyboard area required can be very large and would be impossible to build or use such a keyboard as a practical matter.
As a result, the current techniques employ either a keyboard where several key strokes may be required to enter a desired syllable or a character recognition technique that recognizes entire characters. The keyboard approach provides incomplete visibility of the entire character map at any given point of time. In addition, these keyboards are non-intuitive and can require extensive practice period for proficiency. Further, character entries using such keyboards tend to be very slow. Furthermore, the increasing demand for smaller and smaller devices is driving keyboard designs toward a smaller one-handed keypad making it impractical to use keyboards accommodating such large character sets. This can pose a severe problem for handheld devices, such as PDAs, which currently use graffiti keypads or T9 keyboards.
The character recognition approach requires entering characters using a pen to naturally write an entire character on a graphics tablet. In this approach, the character recognition technique attempts to find the character that most closely matches the strokes entered on the tablet. Most of the current handwritten character recognition techniques recognize neatly printed characters. However, the recognition accuracy of handwritten characters in scripts having complex shapes, such as Indic, Arabic, and the like is significantly poor.
Currently, handwritten character recognition techniques are not robust enough to handle the different writing styles of an arbitrary user of such characters in phonetic scripts and hence tend to have significantly lower recognition accuracy. In addition, the current handwritten character recognition techniques are not capable of handling characters of scripts, such as Indic, Arabic, South-east Asian, and the like, which have complex shapes and require writing these shapes neatly by using strokes in a particular direction and order. Further, the characters in phonetic scripts are generally highly cursive in nature at the syllable level, which makes it even more difficult for the current handwritten character recognition techniques to recognize such handwritten characters.